Ayed A. Salman

Kuwait University | KU · College of Graduate Studies

A great amount of research is focused, nowadays, on experimental, theoretical, and numerical analysis of transient pool boiling. Knowing the minimum film boiling temperature (Tmin) for rods with different substrate materials that are quenched in distilled water pools at various system pressures is known to be a complex and highly non-linear process...

In wireless heterogenous networks, mobile terminals are covered by different wireless networks with varying quality of services to ensure the delivery of different classes of services. In this paper, Particle swarm optimization (PSO) was applied to the distance to ideal alternative (DIA) technique in the framework of network selection in a heteroge...

Sine Cosine Algorithm (SCA) is a newly proposed competent population-based metaheuristic, which has gained a multi-disciplinary interest in solving optimization problems. Like other metaheuristics, the performance of SCA is sensitive to the settings of its parameters and one such parameter is Population Size (PS). There is no one population size th...

Traditional vulnerability scanning methods are time-consuming and indecisive, and they negatively affect network performance by generating high network traffic. In this paper, we present a novel vulnerability scanner that is time-efficient, simple, accurate, and safe. We call it a Calcium Vulnerability Scanner (CVS). Our contribution to vulnerabili...

Creating exams for some subjects is an arduous task that requires analyzing large data sets. Most learning management and examination systems do not support automated exam creation features. In addition, they suffer from numerous downsides such as the need for sophisticated infrastructure requirements (e.g., web server, database server). In this pa...

The multi-battalion search algorithm (MBSA) is a heuristic algorithm used to solve optimization problems by mimicking battlefield strategies and tactics to find the most optimal solutions. The searching strategy for the MBSA consists of dividing a search space into several battalions or cells. The algorithm saves time by performing a parallel searc...

Cuckoo Search (CS) is a recent swarm intelligence based meta-heuristic optimization algorithm that has shown excellent results for a broad class of optimization problems in diverse fields. However, CS is generally compute intensive and slow when implemented in software requiring large number of fitness function evaluations to obtain acceptable solu...

Digital forensics education faces numerous challenges that hinder effective knowledge transfer to students. For instance, FAT file systems illustrations can be a protracted process in a practical session: hopping amongst FAT table, boot sector, root directory, and data area take long time. This is frustrating for students and arduous for the instru...

Cloud computing has been the trending model for storing, accessing and modifying the data over the Internet in the recent years. Rising use of the cloud has generated a new concept related to the cloud which is cloud forensics. Cloud forensics can be defined as investigating for evidence over the cloud, so it can be viewed as a combination of both...

Environment is increasingly becoming an important issue in the world of politics and global economy as well as people's life. Environmental deterioration resulted from human activity; misuse of natural resources. Pollution is now a global issue that ecologically, economically and politically requires imminent, intelligent and global solutions. This...

Environment is increasingly becoming an important issue in the world of politics and global economy as well as people's life. Environmental deterioration resulted from human activity; misuse of natural resources. Pollution is now a global issue that ecologically, economically and politically requires imminent, intelligent and global solutions. This...

Comprehensive Learning Particle Swarm Optimizer (CLPSO) is a state-of-the-art variant of PSO, which maintains the diversity of its swarm by learning from different exemplars on different dimensions. Preserving the swarm diversity enables CLPSO to address the premature convergence problem associated with the canonical PSO. In this paper, the perform...

In this paper a new effective and scalable differential evolution algorithm is proposed for optimizing the Satellite Broadcast’s Scheduling problem (SBS). The satellite broadcast’s scheduling optimization problem is known to be an NP-complete problem in which the aim is to find a valid broadcasting pattern to earth-stationed terminals which maximiz...

Harmony search (HS) is an optimization technique that uses several operators such as pitch adjustments to provide local improvement to candidate solutions during the optimization process. A standard pitch adjustment operator is known to be inefficient for binary domain optimization problems. A novel adaptive probabilistic harmony search (APHS) algo...

An adaptive variant of Comprehensive Learning Particle Swarm Optimizer (CLPSO) is proposed in this paper. The proposed method, called Fuzzy-Controlled CLPSO (FC-CLPSO), uses a fuzzy controller to tune the probability learning, inertia weight and acceleration coefficient of each particle in the swarm. The FC-CLPSO is compared with CLPSO and SPSO2011...

The term Cloud Computing is not something that appeared overnight, it may come from the time when computer system remotely accessed the applications and services. Cloud computing is Ubiquitous technology and receiving a huge attention in the scientific and industrial community. Cloud computing is ubiquitous, next generation's in-formation technolog...

Cloud computing describes highly scalable computing resources provided as an external service via the internet. Economically, the main feature of cloud computing is that customers only use what they need, and only pay for what they actually use. Resources are available to be accessed from the cloud at any time, and from any location via the interne...

Optimizing problems are problems of finding the best feasible solution in a set of solutions. Multi Battalion Search Algorithm (MBSA) is a heuristic algorithm used to solve optimization problems by simulating battlefield strategies and tactics to find optimal or near optimal solutions. The strategy of search in MBSA consists of dividing search spac...

In this paper, we investigate the use of low-discrepancy sequences to generate an initial population for population-based optimization algorithms. Previous studies have found that low-discrepancy sequences generally improve the performance of a population-based optimization algorithm. However, these studies generally have some major drawbacks like...

In this paper we propose a simple, fast and scalable heuristic for solving the Satellite Broadcast Scheduling problem. The satellite broadcast scheduling optimization problem is known to be an NP-complete problem, where the aim of such scheduling problem is to find a valid broadcasting scheduling pattern that maximizes the number of broadcasting's...

Stochastic Diffusion Search (SDS) is a population-based, naturally inspired search and optimization algorithm. It belongs to a family of swarm intelligence (SI) methods. SDS is based on direct (one-to-one) communication between agents. SDS has been successfully applied to a wide range of optimization problems. In this paper we consider the SDS meth...

This paper presents an improved version of a music-inspired meta-heuristic algorithm, Harmony Search (HS), for successfully solving the NP-complete task assignment problem (TAP) in distributed computing systems. Task assignment is an important and core step in distributed systems where program tasks must be properly allocated to the processors to e...

This paper presents an attempt to use a Differential Evolution algorithm to solve the NP-complete course scheduling problem. The course scheduling problem involves assigning courses, faculty members, and rooms to timeslots, subject to preset constraints. Categorizing the constraints as hard and soft, the goal of this type of problem is satisfying h...

Packet radio networks have attracted many applications due to their flexible structure and ability to provide high-speed wireless communication between nodes distributed over a large region. Broadcast scheduling is commonly used to find a collision-free time division multiple access protocol frame that schedule transmissions for all nodes in a mini...

Harmony Search (HS) is a new meta-heuristic algorithm imitating the music improvisation process where musicians search for a better state of harmony. In this paper, a new improvisation scheme is proposed that explicitly uses a probabilistic model of candidate solutions stored in the harmony memory. Pitch adjustment uses a probability distribution t...

Face recognition is the most complex approach for identifying people in biometrics. Other biometric approaches, such as iris recognition, finger print, etc, for human recognition require close contact with the person. Traditional algorithm for face recognition are concerned with both accuracy and timing. Timing issue is more critical when dealing w...

In computer security terminologies, SQL Injection Attacks (SQLIAs) are attacks that pose a security threats to web applications by manipulating, modifying, retrieving or destructing sensitive information underlying database server through web applications. This type of attacks could compromise data confidentiality, integrity and availability of dat...

The prospect of establishing wireless local area network (WLAN) has many major issues, one of which happens to be the determination of the best placement of the access points (AP). This problem consists of determining the optimal locations for AP in order to maximize the coverage area with a minimum number of access points. Because there happens to...

The research literature provides strong evidence that characteristics of buildings and their indoor environments influ-ence the prevalence of several adverse health effects. Kuwait is considered one of the countries with harshest weather conditions. It is estimated that Kuwaitis spend most of their times indoors. Indoor environments quality should...

Harmony search (HS) is a recently spotlighted metaheuristic optimisation method imitating the music improvisation process where musicians improvise their instruments' pitches searching for a perfect state of harmony. In this paper, the effect of using opposition-based learning and quadratic interpolation is investigated. Three variants are proposed...

Satellite communications technology has a tremendous impact in refining our world. The frequency assignment problem is of a fundamental importance when it comes to providing high-quality transmissions in satellite communication systems. The NP-complete frequency assignment problem in satellite communications involves the rearrangement of frequencie...

Department's course scheduling problem involves assigning courses, timeslots, and rooms to faculty members. However, due to the large number of constraints that must be handled, searching for an optimal solution for course scheduling problem is considered to be a complex and a time-consuming task. Methods used nowadays in many educational institute...

Many real-world optimization problems are constrained problems that involve equality and inequality constraints. CODEQ is a new, parameter-free meta-heuristic algorithm that is a hybrid of concepts from chaotic search, opposition-based learning, differential evolution and quantum mechanics. The performance of the proposed approach when applied to f...

We consider the problem of disk scheduling to enhance disk performance. We introduce a new algorithm (S- LOOK) for offline optimal performance, in terms of total seek time, and test it in online situations using a preemptive approach. In addition, we build a simulator for disk requests generation (DiskSims) to experimentally test our proposed algor...

The barebones differential evolution (BBDE) is a new, almost parameter-free optimization algorithm that is a hybrid of the barebones particle swarm optimizer and differential evolution. Differential evolution is used to mutate, for each particle, the attractor associated with that particle, defined as a weighted average of its personal and neighbor...

Differential evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming ta...

Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets, so that the data i...

Differential evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming ta...

In this study we introduce a new idea of utilizing algorithms from the Computational Intelligence community in building accurate models for saline water evaporation rates. Three experimental methods were used to measure the evaporation rate for different brine concentrations, different water and air temperatures, and different air velocities. A lar...

A new, almost parameter-free optimization algorithm is developed in this paper as a hybrid of the barebones particle swarm optimizer (PSO) and differential evolution (DE). The DE is used to mutate, for each particle, the attractor associated with that particle, defined as a weighted average of its personal and neighborhood best positions. Results o...

The performance of two variants of particle swarm optimization (PSO) when applied to integer programming problems is investigated. The two PSO variants, namely, barebones particle swarm (BB) and the exploiting barebones particle swarm (BBExp) are compared with the standard PSO and standard differential evolution (DE) on several integer programming...

Without Abstract

Differential evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. A fully informed DE (FIDE) is proposed in this paper where each member of the individual's neighborhood contributes to the new mutant vector....

This paper investigates the performance of Self-adaptive Differential Evolution (SDE) using a ring neighborhood topology, and compares the results with other well-known DE approaches. The experiments conducted show that using the ring topology with SDE generally improves the performance of SDE in the benchmark functions.

A new dynamic clustering approach (DCPSO), based on particle swarm optimization, is proposed. This approach is applied to image segmentation. The proposed approach automatically determines the “optimum” number of clusters and simultaneously clusters the data set with minimal user interference. The algorithm starts by partitioning the data set into...

A simple and easily programmed code is proposed for estimating the scaling potential for different scaling species, that are expected to be precipitated in reverse osmosis (RO) systems, as calcium carbonate, calcium sulfate, barium sulfate, calcium fluoride and strontium sulfate scaling.All types of scaling in RO system will be included in the simp...

A color image quantization algorithm based on Particle Swarm Optimization (PSO) is developed in this paper. PSO is a population-based optimization algorithm modeled after the simulation of social behavior of bird flocks and follows similar steps as evolutionary algorithms to find near-optimal solutions. The proposed algorithm randomly initializes e...

An image clustering method that is based on the particle swarm optimizer (PSO) is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together with similar image primitives. To illustrate its wide applicability, the proposed image classifier has been applied to synthetic, MRI...

A clustering method that is based on differential evolution is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together similar patterns. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is invest...

Differential Evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming ta...

A new dynamic clustering approach (DCPSO), based on Particle Swarm Optimization, is proposed. This approach is applied to unsupervised image classification. The proposed approach automatically determines the "optimum" number of clusters and simultaneously clusters the data set with minimal user interference. The algorithm starts by partitioning the...

An end-member selection method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed in this paper. The algorithm uses the K-means clustering algorithm and a method of dynamic selection of end-members subsets to find the appropriate set of end-members for a given set of multispectral images. The proposed algorithm ha...

Task assignment is one of the core steps to effectively exploit the capabilities of distributed or parallel computing systems. The task assignment problem is an NP-complete problem. In this paper, we present a new task assignment algorithm that is based on the principles of particle swarm optimization (PSO). PSO follows a collaborative population-b...

Problem-specific knowledge is often implemented in search algorithms using heuristics to determine which search paths are to be explored at any given instant. As in other search methods, utilizing this knowledge will more quickly lead a genetic algorithm (GA) towards better results. In many problems, crucial knowledge is not found in individual com...

Problem-specific knowledge is often implemented in search algorithms using heuristics to determine which search paths are to be explored at any given instant. As in other search methods, utilizing this knowledge will lead a genetic algorithm (GA) faster towards better results. In many problems, crucial knowledge is to be found not in individual com...

Differential Evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming ta...

A new automatic image generation tool is proposed in this paper tailored specifically for verification and comparison of different image clustering algorithms. The tool can be used to produce different images (in raw format) with different criteria based on user specification. The user specifies the number of clusters to be included in the image al...